Surgical instrument detection and tracking technologies: Automating dataset labeling for surgical skill assessment
Problem
Assessing surgeons' technical skill from video is time-consuming and subjective, and labeling raw surgical video datasets for skill analysis is hard.
Why it matters
Better skill assessment enables continuous training and feedback, reducing adverse surgical outcomes.
What the AI did
Vision-based instrument detection and tracking algorithms analyze endoscope images to extract instrument movement and orientation, which can be used to automatically label surgical video datasets.
Results
not stated in source
Impact
The evidence only supports an overview of detection/tracking technologies, not a demonstrated labeling result.
Record
| Venue | Frontiers in Robotics and AI |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Shubhangi Nema, Leena Vachhani |
| Citations | 38 (OpenAlex, at import time) |
| References | 63 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3389/frobt.2022.1030846 |
| Open access | gold |
| Keywords | surgical instruments, detection and tracking, skill assessment, dataset labeling, minimally invasive surgery |
Importance rating
real but narrower advance
Abstract claims better skill assessment reduces adverse surgical outcomes and enables training feedback; no scale or outcome numbers given, review-level evidence only.